Role of trade agreements in the global cereal market and implications for virtual water flows

Understanding the dynamics of food trade, which involves a corresponding virtual trade in environmental resources, is relevant for its effects on the environment. Among the socioeconomic factors driving the international food market, trade agreements play a significant yet poorly understood role in facilitating access to worldwide trade. Focusing on the global trade of grain from 1993 to 2015, we investigate the role of trade agreements in activating new linkages and increasing traded volumes and their environmental implications. Through a data-driven approach, we show that the activation of a trade agreement among countries induces a more than six-fold increase in the probability of establishing a new link. Also, the presence of a trade agreement over time, not just its activation, relates to a more stable market since it reduces the probability of link deactivation by more than half. The trade links covered by agreements show larger flows and smoother inter-annual fluctuations. Furthermore, trade agreements encourage the development of more water-efficient flows by stimulating the exchange of crops with high water productivity values. The average economic water productivity of crops traded under trade agreements increases by 62% when considering total virtual water and even by 93% when focusing on blue water.


Russian Federation -Azerbaijan
Turkey-EFTA   Table 3 shows an example of a contingency table where there are two events (A and B) that occur at two different times (t and t-1). This table shows the combined frequencies of events in the two different years: the first cell, for example, informs that in 10% of the total cases, event A occurred both in year t and in year t-1. By definition, the cell values sum to 100%. In our study, we use this tool to investigate whether an agreement influences the activation of a trade link and to display the percentage of links that persisted between years. Thus, event A represents the absence of trade ties with cereals for our investigation. In contrast, event B represents the presence of trade ties, considering two successive years (t-1 and t). We apply contingency tables by dividing the country pairs (i, j) into three different sets described in the Methods section.
To clarify the subdivision and number of links in the three groups considered and described in subsection 3.1 of the Methods, the Figure 1 provides a descriptive view of the subsets:

(d) Flow variation and water productivity for blue and green virtual water.
The virtual water content can be quantified in green and blue water components, depending on whether the water is contributed by rainwater or by surface and groundwater used for irrigation and food processing. In this subsection we carry out the analysis by taking the two components of blue water and green water separately. Table 4 shows in column (a) the results for total virtual water (blue and green together), in column (b) for blue water, and finally in column (c) for green water. Table 4. Average values of virtual water trade flows and flow variation index ρ i j considering the total virtual water (blue and green together), and separately. The bar indicates the average operator. The subscript w indicates the weighted average, where weights correspond to the flows at time t-1 (i.e.,V i j (t − 1)). Values of ρ i j is reported in percentage point (p.p).

(c) VW m 3 green
Operational Activation V i j (t) 1.82 · 10 8 | ρ i j | w 44.74 p.p Trade Agreement in t-1 and t The average volume of blue water when a trade agreement is present over time is slightly higher than when there is no agreement. This means that the differences in trading volumes observed in total water between flows covered and not covered by trade agreements, in addition to being smaller than those found in US$ and Kcal, are also almost exclusively due to green water.
(e) Regional trade agreements presenting negative percentage variation of the flows. Table 5 displays the treaties that show a flow decrease from the year before the entry into force of the agreement, to the year after ratification. Notice that the ρ a values are flow percentage variations in comparison to the fluctuations of the flows registered on links not covered by trade agreements. As mentioned in the Discussion section, a negative ρ a value does not necessarily highlight a decrease, but indicates a lower increase compared to the average variation of non-agreement trade relationships. For this reason, differently from Table ?? we have also included the percentage change in the trade agreement (∆ a ). In this way, we observe that some agreements, i.e., Japan -Thailand, register a flow increase of 36% compared to the year before the treaty was signed. The respective ρ a value is -14 (p.p), which means that the change related to the trade treaty was smaller than the increase in flows that occurred in all links not covered by trade agreements. Table 5. Flow values in millions of dollars and negative percent changes ρ a for each trade agreement. The colors are assigned according to the same procedure as in the Table ??. For each geographic area, trade agreements are sorted in descending order by flow value ($ million). The color and the orientation of the arrows classifies the percentage changes into three categories: gray for a slight decrease (≤ −10% decrease in flow intensity), yellow for strong decrease (decrease ≤ -10% and ≥ -50%), and red for extreme decrease (decrease < -50%).